{"id":"W4241629975","doi":"10.1515/iupac.81.0490","title":"Interspecific Interaction","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Ecotoxicology; Relation (database); Interspecific competition; Ecology; Computer science; Biology; Data mining; Linguistics; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001516661,0.001929894,0.002270442,0.00303489,0.001431424,0.003382232,0.003282581,0.002297491,0.09415364],"category_scores_gemma":[0.01111052,0.0006656036,0.002852096,0.005541178,0.0004926238,0.002791492,0.003194799,0.002608475,0.07291661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001757323,"about_ca_system_score_gemma":0.002691979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01682507,"about_ca_topic_score_gemma":0.03991484,"domain_scores_codex":[0.9968842,0.0005256088,0.0004058804,0.001249301,0.000564406,0.0003706513],"domain_scores_gemma":[0.9952951,0.001792001,0.0006865262,0.001162093,0.0007625215,0.0003016842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003897699,0.00008559063,0.01334784,0.003210892,0.0003510692,0.0001565608,0.00006684196,0.0006691183,0.0004236056,0.002714498,0.9620334,0.01655071],"study_design_scores_gemma":[0.000136804,0.00004217559,0.01503323,0.0008101394,0.0002206406,0.0004039868,0.0000991161,0.0004960936,0.0004484213,0.004472328,0.9777817,0.00005550606],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006528939,0.0009688487,0.00031043,0.0001984824,0.000111587,0.00002960075,0.9943973,0.0002464732,0.003084389],"genre_scores_gemma":[0.002637352,0.000454955,0.0007495311,0.0003261364,0.00003118897,0.0001775628,0.9926519,0.00008308703,0.002888402],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09415364,"threshold_uncertainty_score":0.3149753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799682600308668,"score_gpt":0.4068394009481254,"score_spread":0.3888425749450387,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}